31 #ifndef GRT_REGRESSIFIER_HEADER
32 #define GRT_REGRESSIFIER_HEADER
35 #include "../DataStructures/ClassificationData.h"
36 #include "../DataStructures/TimeSeriesClassificationData.h"
40 #define DEFAULT_NULL_LIKELIHOOD_VALUE 0
41 #define DEFAULT_NULL_DISTANCE_VALUE 0
177 std::string regressifierType;
182 static StringRegressifierMap *getMap() {
184 return stringRegressifierMap;
188 static StringRegressifierMap *stringRegressifierMap;
189 static UINT numRegressifierInstances;
194 template<
typename T >
Regressifier *newRegressionModuleInstance() {
return new T; }
196 template<
typename T >
200 getMap()->insert( std::pair< std::string,
Regressifier*(*)() >(newRegresionModuleName, &newRegressionModuleInstance< T > ) );
206 #endif //GRT_REGRESSIFIER_HEADER
Vector< MinMax > getOutputRanges() const
std::map< std::string, Regressifier *(*)() > StringRegressifierMap
virtual bool train(ClassificationData trainingData)
Regressifier * createNewInstance() const
static Regressifier * createInstanceFromString(const std::string ®ressifierType)
bool copyBaseVariables(const Regressifier *regressifier)
static Vector< std::string > getRegisteredRegressifiers()
virtual bool deepCopyFrom(const Regressifier *regressifier)
bool saveBaseSettingsToFile(std::fstream &file) const
This is the main base class that all GRT machine learning algorithms should inherit from...
std::string getRegressifierType() const
bool loadBaseSettingsFromFile(std::fstream &file)
Vector< MinMax > getInputRanges() const
Regressifier * deepCopy() const
virtual ~Regressifier(void)
const Regressifier & getBaseRegressifier() const
VectorFloat getRegressionData() const